Results for “natural-language”

Generates custom Semgrep rules from natural language descriptions of vulnerability patterns, verifying syntax with semgrep --validate and testing against sample fixtures with semgrep --test.

Translate natural language into SQL queries, optimize database performance, generate migrations, explore schemas, and work with ORMs across PostgreSQL, MySQL, SQLite, and SQL Server.

Automate web browser interactions using natural language via CLI commands. Supports local and remote Browserbase sessions with CAPTCHA solving, residential proxies, and session persistence.

Introduces Prompt-Driven Development (PDD), a methodology that uses version-controlled natural-language prompts as the source of truth for generating code, tests, and documentation, with a practical adoption roadmap.

Generate Excalidraw diagrams from natural language descriptions, supporting flowcharts, relationship diagrams, mind maps, architecture diagrams, and more. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.

Controls a live browser session on scraped pages to click, fill forms, navigate flows, and extract data using natural language prompts or code, including support for authenticated sessions via profiles.

Generates high-fidelity UI screens, multi-page websites, DESIGN.md documentation, and React components from natural-language prompts using the Stitch MCP server, with support for prompt refinement and walkthrough video creation via Remotion.

Expert 10x engineer skill for interpreting and implementing code from shorthand, quasi-code, and natural language descriptions. Use when collaborators provide incomplete code snippets, pseudo-code, or descriptions with potential typos or incorrect terminology. Excels at translating non-technical or semi-technical descriptions into production-quality code.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Detects and fixes patterns including: inflated symbolism, promotional language, superficial analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Starts working on a new feature branch using git-flow. This skill should be used when the user asks to "start a feature", "create feature branch", "begin new feature", "git flow feature start", or wants to start a new feature. Accepts either a branch name or a natural-language description.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Detects and fixes patterns including: inflated symbolism, promotional language, superficial analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Begins a new version release using git-flow. This skill should be used when the user asks to "start a release", "create release branch", "prepare a release", "git flow release start", or wants to begin a new version release. Accepts either an explicit version or a natural-language description of the release.

Begins a hotfix for a production issue using git-flow. This skill should be used when the user asks to "start a hotfix", "create hotfix branch", "fix a critical bug", "git flow hotfix start", or wants to begin a hotfix. Accepts either an explicit version or a natural-language description of the fix.

Build ontology logs (ologs) from problem descriptions using categorical foundations. Use when designing problem taxonomies, classifying tasks for routing, building knowledge libraries, establishing formal analogies between domains via functor search, or translating between natural language and database schemas. NOT for OWL/RDF ontology work, query tuning, or graph modeling without functional-arrow discipline.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Create or update a MoonSpec feature specification from a natural language feature request or referenced declarative design. Use when the user asks to run or reproduce `/moonspec.specify`, create a `spec.md`, initialize a feature directory under `specs/`, preserve the original feature request for later verification, map source design requirements, or generate a single-story MoonSpec specification with a requirements quality checklist.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer

Produce a plain-language manual test plan from the context supplied to it — an executive summary, a high-level list of named tests, and a detail section per test with the steps a person follows by hand and the outcomes they should expect. Use when you want to create, draft, generate, or outline a manual test plan, manual QA steps, hands-on verification steps, or an acceptance walkthrough for a feature, change, branch, plan, or PR. When the plan holds more than five tests and at least two natural categories emerge, both the test list and the detail sections are organized under plain-language categories. When nothing in the supplied context can be manually tested, it says so and asks for more context instead of producing a document. Does not analyze code for automated test coverage gaps — use automated-test-planning. Does not write test code — use tdd. Does not review code quality — use code-review. Does not stress-test an existing plan — use iterative-plan-review.

The craft of designing icons that communicate instantly across cultures, contexts, and scales. Icon design bridges semiotics, cognitive psychology, and visual craft to create symbols that users understand without thinking. Great icons are invisible in the best way - they convey meaning so naturally that users never pause to decode them. This skill covers icon grid systems, optical alignment, stroke consistency, metaphor selection, scalability across sizes, SVG optimization, and icon set coherence. The best icon designers understand that icons are a visual language - each icon must speak the same dialect while carrying its own distinct meaning. Use when "icon, iconography, symbol, glyph, icon set, icon library, pictogram, svg icon, icon grid, icon pack, feather icons, lucide, phosphor, heroicons, icon system, icon style, icons, iconography, svg, symbols, glyphs, pictograms, ui-icons, icon-set, visual-design, design-system" mentioned.

Generate browser walkthrough recordings as GIF and MP4 from a natural language description. Use this skill whenever the user wants to record a browser walkthrough, create a product demo, document a UI flow, generate onboarding visuals, or produce a "how to" screen recording for any web app. Triggers include: "record a walkthrough of", "create a GIF showing how to", "make a demo of", "capture the flow for", "show how [feature] works in the browser", "document this UI flow", or any request combining a browser/web app with recording/capture/demo output. The skill generates a local Node.js/TypeScript project that the user runs on their machine using Stagehand + Playwright for AI-controlled browser automation, and gifski/ffmpeg for output. For quick one-off captures in the user's live browser session, prefer the claude-in-chrome gif_creator tool instead; use this skill when a re-runnable script, MP4, high-quality GIF, clean-profile recording, or CI repeatability is needed.